Repurposing FDA-approved compounds to target JAK2 for colon cancer treatment.

Colorectal cancer is one of the common cancers worldwide and the second leading cause of cancer-related death. The current treatment has the inherent drawbacks and there is a need of developing a new treatment. Interleukin-6 a pleiotropic cytokine involved in immune regulation and activation of JAK2/STAT3 pathway in colorectal cancer. JAK2/STAT3 signaling pathway functions as a critical regulator of cell growth, differentiation, and immune expression. The abnormality in the JAK2/STAT3 pathway is involved in the tumorigenesis of colon cancer including apoptosis. In this study, we identified novel inhibitors for JAK2 protein by performing virtual screening against FDA-approved compounds. To address the selectivity issue, we implemented cross-docking method followed by DFT calculations to understand the chemical reactivity of the identified compounds. Additionally, molecular dynamics (MD) simulations were performed for the top FDA compounds against JAK2 to understand the molecular interactions and structural stability of the complex over a period of 200 ns. Our results indicated that ergotamine, entrectinib, exatecan, dihydroergotamine, and paritaprevir can be used as alternative drugs for colon cancer. In addition, ergotamine was found to efficiently lower the cell viability with IC50 values of 100 µM on colon cancer cell lines. The long-term inhibitory effect of the ergotamine led to a decrease in colony size, and the toxicity properties were studied using hemolysis assay. Our study shows the potential of targeting JAK2 as a novel approach to colon cancer treatment, and demonstrate that ergotamine as a promising effects as an anti-cancer drug.

MLASM: Machine learning based prediction of anticancer small molecules.

Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research.

Inducing Cytotoxicity in Colon Cancer Cells and Suppressing Cancer Stem Cells by Dolasetron and Ketoprofen through Inhibition of RNA Binding Protein PUM1.

Clinical trials of new drugs often face a high failure rate of approximately 45 percent due to safety and toxicity concerns. Repurposing drugs with well-established safety profiles becomes crucial in addressing this challenge. Colon cancer ranks as the third most prevalent cancer and the second leading cause of cancer related mortality worldwide. This study focuses on the RNA-binding protein pumilio1 (PUM1), a member of the PUF family involved in post-transcriptional gene expression regulation. By utilizing molecular docking techniques and FDA-approved drugs, potential inhibitors against PUM1 were identified. Notably, dolasetron and ketoprofen demonstrated promising results, exhibiting strong binding affinity, hydrophobic interactions, and favorable chemical reactivity according to Conceptual-DFT calculations. Both compounds effectively reduced cell viability, with IC50 values of 150 µM and 175 µM, respectively and shows long term inhibitory effects as seen by reduced in number of colonies. Moreover, they exhibited inhibitory effects on colon cancer stem cells, as indicated by reduced colonospheroid size and numbers. Apoptosis is induced by these compounds and has triggered activation of executioner caspase 3/7 in HCT116 cells which is evident through a caspase 3/7 assay and AO/EB staining, while the non-toxic effect of these compounds was evident from viability against non-cancerous cell line and hemolysis assay. Additionally, the treatment group showed a significant decrease in PUM1 and cancer stem cell markers expression compared to the control group. In conclusion, this study highlights the potential of targeting PUM1 as a novel approach to colon cancer treatment. Dolasetron and ketoprofen demonstrate promise as effective anti-cancer and anti-cancer stem cell drugs, inducing apoptosis in colon cancer cells through inhibition of PUM1.

Morin inhibits colon cancer stem cells by inhibiting PUM1 expression in vitro.

Over the last few decades, the number of people diagnosed with cancer has increased dramatically every year, making it a major cause of mortality today. Colon cancer is the third most common cancer worldwide, and the second in mortality rate. Current cancer treatment fails to treat colon cancer completely due to the remains of Cancer Stem Cells (CSCs). Morin flavonoid present in figs (Ficus carica) and other plant sources, was found to have an anti-proliferative effect on the colon cancer model and cell line, but it is not studied for its effect on the colon CSCs. In this study, we have tested the potency of morin to inhibit CSCs. We found that morin has significantly reduced colon cancer cell proliferation, colony formation, migration, and colonospheroid formation in a dose-dependent manner. Pumilio-1 (PUM1) has been shown to play an important role in colon CSCs maintenance. We found that morin has a good binding affinity with PUM1 protein with one hydrophobic and two hydrogen bond interactions. Further, the immunofluorescence results have also shown a reduction in PUM1 expression in colon cancer cell lines after morin treatment. CD133 is overexpressed in colon CSCs and morin treatment has reduced the CD133 expression in HCT116 and CT26 colon cancer cell lines. Our research outcome has explored the anti-cancer stem cell potency of morin via targeting the PUM1 protein and further reducing the colon spheroids formation and reducing the CD133 expression in colon cancer cells.

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction.

Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training dataset, the study explored a wide range of feature encodings and developed their respective models using seven different conventional classifiers. Subsequently, a subset of encoding-based models was selected for each classifier based on their performance, whose predicted scores were concatenated and trained through a convolutional neural network (CNN), whose corresponding predictor is named MLACP 2.0. The evaluation of MLACP 2.0 with a very diverse independent dataset showed excellent performance and significantly outperformed the recent ACP prediction tools. Additionally, MLACP 2.0 exhibits superior performance during cross-validation and independent assessment when compared to CNN-based embedding models and conventional single models. Consequently, we anticipate that our proposed MLACP 2.0 will facilitate the design of hypothesis-driven experiments by making it easier to discover novel ACPs. The MLACP 2.0 is freely available at https://balalab-skku.org/mlacp2.

Identification of Potent and Selective JAK1 Lead Compounds Through Ligand-Based Drug Design Approaches.

JAK1 plays a significant role in the intracellular signaling by interacting with cytokine receptors in different types of cells and is linked to the pathogenesis of various cancers and in the pathology of the immune system. In this study, ligand-based pharmacophore modeling combined with virtual screening and molecular docking methods was incorporated to identify the potent and selective lead compounds for JAK1. Initially, the ligand-based pharmacophore models were generated using a set of 52 JAK1 inhibitors named C-2 methyl/hydroxyethyl imidazopyrrolopyridines derivatives. Twenty-seven pharmacophore models with five and six pharmacophore features were generated and validated using potency and selectivity validation methods. During potency validation, the Guner-Henry score was calculated to check the accuracy of the generated models, whereas in selectivity validation, the pharmacophore models that are capable of identifying selective JAK1 inhibitors were evaluated. Based on the validation results, the best pharmacophore models ADHRRR, DDHRRR, DDRRR, DPRRR, DHRRR, ADRRR, DDHRR, and ADPRR were selected and taken for virtual screening against the Maybridge, Asinex, Chemdiv, Enamine, Lifechemicals, and Zinc database to identify the new molecules with novel scaffold that can bind to JAK1. A total of 4,265 hits were identified from screening and checked for acceptable drug-like properties. A total of 2,856 hits were selected after ADME predictions and taken for Glide molecular docking to assess the accurate binding modes of the lead candidates. Ninety molecules were shortlisted based on binding energy and H-bond interactions with the important residues of JAK1. The docking results were authenticated by calculating binding free energy for protein-ligand complexes using the MM-GBSA calculation and induced fit docking methods. Subsequently, the cross-docking approach was carried out to recognize the selective JAK1 lead compounds. Finally, top five lead compounds that were potent and selective against JAK1 were selected and validated using molecular dynamics simulation. Besides, the density functional theory study was also carried out for the selected leads. Through various computational studies, we observed good potency and selectivity of these lead compounds when compared with the drug ruxolitinib. Compounds such as T5923555 and T5923531 were found to be the best and can be further validated using in vitro and in vivo methods.

Radio-protective efficacy of Gymnema sylvestre on Pangasius sutchi against gamma (60Co) irradiation.

PURPOSE: Freshwater fish Pangasius sutchi was used in this study as a vertebrate model. We evaluated the induction of certain antioxidant enzymes in various vital organs. The radioprotective efficacy of Gymnema sylvestre leaves extract (GS) [25 mg/kg Body Weight (B.W)] and its bioactive compound Gymnemagenin (GG) [0.3 mg/kg B.W] was compared with Amifostine (Ami), the only radioprotector clinically approved by the US-FDA [Ami- 83.3 mg/kg B.W] against different doses of gamma radiation - 60Co (Lethal Dose: LD30-9.2 Gy, LD50-10.2 Gy and LD70-11.4 Gy). MATERIALS AND METHODS: This study was done via stress marker enzymes, cell cycle analysis (CCA) and DNA damage assay prediction with molecular docking, which are reported here for the first time. The results indicate an elevated LPO level and decreased level of CAT, SOD and GSH due to oxidative stress initiation by 60Co Ionizing Radiation (IR) on 4th day and slightly reduced on 32nd day while the reverse observed when the fishes were pretreated with Ami, GS and GG. Similarly, CCA and dead/live cells counts were conducted with pretreatment of Ami, GS and GG against 60Co IR dose (LD50-10.2 Gy). RESULTS: In CCA, G0/G1 phase was observed to be the highest in Ami and lowest in GG, against 60Co IR doses 10.2 Gy which was 51.76 ± 7.55. The dead cells range observed in pretreated group of Ami, GS and GG was lowest in Ami and highest in GG and live cells (highest in Ami and lowest in GG) as compared to 60Co IR group (86.43 ± 3.42 and 8.77 ± 5.95). Thus, antioxidant profile improvement by oxidative stress reduction and gradual progression of different phases of cell cycle except the apoptotic phase along with the live cells counts indicates that the radio-protective efficacy of GS is similar to Ami. CONCLUSION: Predictive assessment was carried out by docking of Ami, various components of GS with p53, NF-κß cells and Rad51 proteins structures responsible for CCA, apoptosis and repair mechanism. These structural proteins were docked with other structural proteins like USP7, TNF-α and partner and localizer of BRCA2 associated (PALB2/BRCA2) complex which made us perform these systemic efforts to find the functional activity of these known radio-protectants.

Antiviral Essential Oil Components Against SARS-CoV-2 in Pre-procedural Mouth Rinses for Dental Settings During COVID-19: A Computational Study.

COVID-19 mainly spreads through cough or sneeze droplets produced by an infected person. The viral particles are mostly present in the oral cavity. The risk of contracting COVID-19 is high in the dental profession due to the nature of procedures involved that produce aerosols. Along with other measures to limit the risk of infection, pre-procedural mouth rinses are beneficial in reducing the viral particles in the oral cavity. In this study, the antiviral efficacy of essential oil components has been determined specifically against SARS-CoV-2 by molecular docking and conceptual DFT approach. Based on the binding affinities of the components against the receptor binding domain of the S1 glycoprotein, cuminal, carvacrol, myrtanol, and pinocarveol were found to be highly active. The molecular descriptor values obtained through conceptual DFT also indicated the above-mentioned components to be active based on the correlation between the structure and the activity of the compounds. Therefore, pre-procedural mouth rinses with these components included may be specifically suitable for dental procedures during the COVID-19 period.

Pharmacologic downregulation of protein arginine methyltransferase1 expression by adenosine dialdehyde increases cell senescence in breast cancer.

We investigated the role of protein arginine methylation (PAM) in estrogen receptor (ER)-positive breast cancer cells through pharmacological intervention. Tamoxifen (TAM) or adenosine dialdehyde (ADOX), independently, triggered cell cycle arrest and down-regulated PAM, as reduced protein arginine methyltransferase1 (PRMT1) mRNA and asymmetric dimethylarginine (ADMA) levels. Synergistic effect of these compounds elicited potent anti-cancer effect. However, reduction in ADMA was not proportionate with the compound-induced down-regulation of PRMT1 mRNA. We hypothesized that the disproportionate effect is due to the influence of the compounds on other methyltransferases, which catalyze the arginine dimethylation reaction and the diversity in the degree of drug-protein interaction among these methyltransferases. In silico analyses revealed that independently, ADOX or TAM, binds with phosphatidylethanolamine-methyltransferase (PEMT) or betaine homocysteine-methyl transferase (BHMT); and that the binding affinity of ADOX with PEMT or BHMT is prominent than TAM. These observations suggest that in breast cancer, synergistic effect of ADOX + TAM elicits impressive protective function by regulating PAM; and plausibly, restoration of normal enzyme activities of methyltransferases catalyzing arginine dimethylation could have clinical benefits.

Understanding the structural features of JAK2 inhibitors: a combined 3D-QSAR, DFT and molecular dynamics study.

JAK2 plays a critical role in JAK/STAT signaling pathway and in patho-mechanism of myeloproliferative disorders and autoimmune diseases. Thus, effective JAK2 inhibitors provide a promising opportunity for the pharmaceutical intervention of many diseases. In this work, 3D-QSAR study was performed on a series of 1-amino-5H-pyrido-indole-4-carboxamide derivatives as JAK2 inhibitors to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) models with three different alignment methods. Among the different alignment methods, ligand-based (CoMFA: q2 = 0.676, r2 = 0.979; CoMSIA: q2 = 0.700, r2 = 0.953) and pharmacophore-based alignment (CoMFA: q2 = 0.710, r2 = 0.982; CoMSIA: q2 = 0.686, r2 = 0.960) has produced better statistical results when compared to receptor-based alignment (CoMFA: q2 = 0.507, r2 = 0.979; CoMSIA: q2 = 0.544, r2 = 0.917). Statistical parameters indicated that data are well fitted and have high predictive ability. The presence of electrostatic and hydrophobic field is highly desirable for potent inhibitory activity, and the steric field plays a minor role in modulating the activity. The contour analysis indicates ARG980, ASN981, ASP939 and LEU937 have more possibility of interacting with bulky, hydrophobic groups in pyrido and positive and negative groups in pyrazole ring. Based on our findings, we have designed sixteen molecules and predicted its activity and drug-like properties. Subsequently, molecular docking, molecular dynamics and DFT calculations were performed to evaluate its potency.

Theoretical analysis of somatostatin receptor 5 with antagonists and agonists for the treatment of neuroendocrine tumors.

We report on SSTR5 receptor modeling and its interaction with reported antagonist and agonist molecules. Modeling of the SSTR5 receptor was carried out using multiple templates with the aim of improving the precision of the generated models. The selective SSTR5 antagonists, agonists and native somatostatin SRIF-14 were employed to propose the binding site of SSTR5 and to identify the critical residues involved in the interaction of the receptor with other molecules. Residues Q2.63, D3.32, Q3.36, C186, Y7.34 and Y7.42 were found to be highly significant for their strong interaction with the receptor. SSTR5 antagonists were utilized to perform a 3D quantitative structure-activity relationship study. A comparative molecular field analysis (CoMFA) was conducted using two different alignment schemes, namely the ligand-based and receptor-based alignment methods. The best statistical results were obtained for ligand-based ([Formula: see text], [Formula: see text] = 0.988, noc = 4) and receptor-guided methods (docked mode 1:[Formula: see text], [Formula: see text], noc = 5), (docked mode 2:[Formula: see text] = 0.555, [Formula: see text], noc = 5). Based on CoMFA contour maps, an electropositive substitution at [Formula: see text], [Formula: see text] and [Formula: see text] position and bulky group at [Formula: see text] position are important in enhancing molecular activity.

3D-QSAR studies of JNK1 inhibitors utilizing various alignment methods.

We report our three-dimensional quantitative structure activity relationship (3D-QSAR) studies of the series of anilinopyrimidine derivatives of JNK1 inhibitors. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied using different alignment methods. The ligand-based atom-by-atom matching alignment has produced better values for CoMFA (q(2) = 0.646 and r(2) = 0.983), while in CoMSIA it has achieved only lower statistical values. The pharmacophore-based model has produced (q(2) = 0.568, r(2) = 0.938) and (q(2) = 0.670, r(2) = 0.982) for CoMFA and CoMSIA models, respectively. As the model was based on the receptor-guided alignment, all the compounds were optimized within the receptor, resulting in q(2) = 0.605 and r(2) = 0.944 for CoMFA, and q(2) = 0.587 and r(2) = 0.863 for CoMSIA. Molecular Dynamic simulation studies suggested that the generated models were consistent with the low-energy protein ligand conformation. The CoMFA and CoMSIA contour maps indicated that the substitutions of the electropositive groups in the phenyl ring, and an addition of hydrophobic groups in the pyrimidine ring, are important to enhance the activity of this series. Moreover, the virtual screening analysis against NCI database yields potentials hits, and the results obtained would be useful to synthesize selective and highly potent c-Jun N-terminal kinase 1 analogs.

Prognostic significance of cyclooxygenase-2 and response to chemotherapy in invasive ductal breast carcinoma patients by real time surface plasmon resonance analysis.

Cyclooxygenase-2 (COX-2), an inducible enzyme, has been implicated in the progression and angiogenesis of breast cancer. The aim of the study is to quantify the concentration of COX-2 and its association with clinico-pathological parameters and response to treatment in patients with invasive ductal carcinoma receiving both neo-adjuvant and adjuvant chemotherapy. The level of COX-2 was estimated using a novel biosensor-based surface plasmon resonance technique in serum of 84 patients with breast cancer (48 patients of neo-adjuvant chemotherapy and 36 patients of adjuvant chemotherapy) and 40 age- and gender-matched normal individuals. A significant increase in COX-2 level was observed in patients compared with normal individuals (p>0.0001). The COX-2 level in serum was found to be significantly higher in patients with lymph node involvement (p<0.0061). 68% (33/48) of the patients receiving neo-adjuvant chemotherapy showed significantly (p<0.0025) reduced COX-2 levels. This study shows significant decrease of COX-2 level in patients with breast cancer treated with both neo-adjuvant and adjuvant chemotherapy. Estimation of COX-2 level in serum may serve as a tumor biomarker in patients with breast cancer.

In silico quantitative structure-activity relationship studies on P-gp modulators of tetrahydroisoquinoline-ethyl-phenylamine series.

BACKGROUND: Multidrug resistance (MDR) is a major obstacle in cancer chemotherapy. The drug efflux by a transport protein is the main reason for MDR. In humans, MDR mainly occurs when the ATP-binding cassette (ABC) family of proteins is overexpressed simultaneously. P-glycoprotein (P-gp) is most commonly associated with human MDR; it utilizes energy from adenosine triphosphate (ATP) to transport a number of substrates out of cells against concentration gradients. By the active transport of substrates against concentration gradients, intracellular concentrations of substrates are decreased. This leads to the cause of failure in cancer chemotherapy. RESULTS: Herein, we report Topomer CoMFA (Comparative Molecular Field Analysis) and HQSAR (Hologram Quantitative Structure Activity Relationship) models for third generation MDR modulators. The Topomer CoMFA model showed good correlation between the actual and predicted values for training set molecules. The developed model showed cross validated correlation coefficient (q2) = 0.536 and non-cross validated correlation coefficient (r2) = 0.975 with eight components. The best HQSAR model (q2 = 0.777, r2 = 0.956) with 5-8 atom counts was used to predict the activity of test set compounds. Both models were validated using test set compounds, and gave a good predictive values of 0.604 and 0.730. CONCLUSIONS: The contour map near R1 indicates that substitution of a bulkier and polar group to the ortho position of the benzene ring enhances the inhibitory effect. This explains why compounds with a nitro group have good inhibitory potency. Molecular fragment analyses shed light on some essential structural and topological features of third generation MDR modulators. Fragments analysis showed that the presence of tertiary nitrogen, a central phenyl ring and an aromatic dimethoxy group contributed to the inhibitory effect. Based on contour map information and fragment information, five new molecules with variable R1 substituents were designed. The activity of these designed molecules was predicted by the Topomer CoMFA and HQSAR models. The novel compounds showed higher potency than existing compounds.